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   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "tags": [
     "remove-cell"
    ]
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   "outputs": [],
   "source": [
    "import sys\n",
    "import os\n",
    "if not any(path.endswith('textbook') for path in sys.path):\n",
    "    sys.path.append(os.path.abspath('../../..'))\n",
    "from textbook_utils import *"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "(ch:pandas_aggregating)=\n",
    "# Aggregating\n",
    "\n",
    "This section introduces operations for aggregating rows in a dataframe. Data\n",
    "scientists aggregate rows together to make summaries of data. For instance, a\n",
    "dataset containing daily sales can be aggregated to show monthly sales instead.\n",
    "This section introduces *grouping* and *pivoting*, two common operations\n",
    "for aggregating data.\n",
    "\n",
    "We work with the baby names data, as introduced in the previous section:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Count</th>\n",
       "      <th>Year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Liam</td>\n",
       "      <td>M</td>\n",
       "      <td>19659</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Noah</td>\n",
       "      <td>M</td>\n",
       "      <td>18252</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Oliver</td>\n",
       "      <td>M</td>\n",
       "      <td>14147</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020719</th>\n",
       "      <td>Verona</td>\n",
       "      <td>F</td>\n",
       "      <td>5</td>\n",
       "      <td>1880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020720</th>\n",
       "      <td>Vertie</td>\n",
       "      <td>F</td>\n",
       "      <td>5</td>\n",
       "      <td>1880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020721</th>\n",
       "      <td>Wilma</td>\n",
       "      <td>F</td>\n",
       "      <td>5</td>\n",
       "      <td>1880</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2020722 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           Name Sex  Count  Year\n",
       "0          Liam   M  19659  2020\n",
       "1          Noah   M  18252  2020\n",
       "2        Oliver   M  14147  2020\n",
       "...         ...  ..    ...   ...\n",
       "2020719  Verona   F      5  1880\n",
       "2020720  Vertie   F      5  1880\n",
       "2020721   Wilma   F      5  1880\n",
       "\n",
       "[2020722 rows x 4 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "baby = pd.read_csv('babynames.csv')\n",
    "baby"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Basic Group-Aggregate"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's say we want to find out the total number of babies born as recorded in\n",
    "this data. This is simply the sum of the `Count` column:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "352554503"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "baby['Count'].sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Summing up the name counts is one simple way to aggregate the data---it\n",
    "combines data from multiple rows."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "But let's say we instead want to answer a more interesting question: are US\n",
    "births trending upward over time? To answer this question, we can sum the\n",
    "`Count` column within each year rather than taking the sum over the entire\n",
    "dataset. In other words, we split the data into groups based on `Year`,\n",
    "then sum the `Count` values within each group.\n",
    "This process is depicted in {numref}`Figure %s <fig:groupby-births>`.\n",
    "\n",
    "```{figure} figures/groupby-births.svg\n",
    "---\n",
    "name: fig:groupby-births\n",
    "alt: fig:groupby-births\n",
    "---\n",
    "A depiction of grouping and then aggregating for example data\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We call this operation _grouping_ followed by _aggregating_. In `pandas`,\n",
    "we write: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Year\n",
       "1880     194419\n",
       "1881     185772\n",
       "1882     213385\n",
       "         ...   \n",
       "2018    3487193\n",
       "2019    3437438\n",
       "2020    3287724\n",
       "Name: Count, Length: 141, dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "baby.groupby('Year')['Count'].sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notice that the code is nearly the same as the nongrouped version, except that\n",
    "it starts with a call to `.groupby('Year')`.\n",
    "\n",
    "The result is a `pd.Series` with the total number of babies born for each year in the\n",
    "data. Notice that the index of this series contains the unique `Year` values.\n",
    "Now we can plot the counts over time:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "counts_by_year = baby.groupby('Year')['Count'].sum().reset_index()\n",
    "px.line(counts_by_year, x='Year', y='Count', width=350, height=250)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What do we see in this plot? First, we notice that there seem to be\n",
    "suspiciously few babies born before 1920. One likely explanation is that the\n",
    "SSA was created in 1935, so its data for prior\n",
    "births could be less complete.\n",
    "\n",
    "We also notice the dip when World War II began in 1939, and the\n",
    "post-war baby boomer era from 1946 to 1964."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here's the basic recipe for grouping in `pandas`:\n",
    "\n",
    "```python\n",
    "(baby                # the dataframe\n",
    " .groupby('Year')    # column(s) to group\n",
    " ['Count']           # column(s) to aggregate\n",
    " .sum()              # how to aggregate\n",
    ")\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [],
    "user_expressions": []
   },
   "source": [
    "### Example: Using .value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "One of the more common dataframe tasks is to count the number of times every unique item in a column appears. For example, we might be interested in the number of times each name appears in the  following `classroom` dataframe:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "tags": [
     "remove-cell"
    ]
   },
   "outputs": [],
   "source": [
    "classroom = pd.DataFrame({\n",
    "    'name': ['Eden', 'Sachit', 'Eden', 'Sachit', 'Sachit', 'Luke'],\n",
    "})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "      <td>Sachit</td>\n",
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       "      <td>Sachit</td>\n",
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       "      <th>5</th>\n",
       "      <td>Luke</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     name\n",
       "0    Eden\n",
       "1  Sachit\n",
       "2    Eden\n",
       "3  Sachit\n",
       "4  Sachit\n",
       "5    Luke"
      ]
     },
     "execution_count": 12,
     "metadata": {},
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    }
   ],
   "source": [
    "classroom"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "One way to do this is to use our grouping recipe with the `.size()` aggregation function:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "name\n",
       "Eden      2\n",
       "Luke      1\n",
       "Sachit    3\n",
       "Name: name, dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(classroom\n",
    " .groupby('name')\n",
    " ['name']\n",
    " .size()\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This operation is so common that `pandas` provides a shorthand—the `.value_counts()` method for `pd.Series` objects:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "name\n",
       "Sachit    3\n",
       "Eden      2\n",
       "Luke      1\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "classroom['name'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "By default, the `.value_counts()` method will sort the resulting series from highest to lowest number, making it convenient to see the most and least common values. We point out this method because we use it often in other chapters of the book."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Grouping on Multiple Columns\n",
    "\n",
    "We pass multiple columns into `.groupby` as a list to group by multiple\n",
    "columns at once. This is useful when we need to further subdivide our groups.\n",
    "For example, we can group by both year and sex to see how many male and female\n",
    "babies were born over time:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Year  Sex\n",
       "1880  F        83929\n",
       "      M       110490\n",
       "1881  F        85034\n",
       "              ...   \n",
       "2019  M      1785527\n",
       "2020  F      1581301\n",
       "      M      1706423\n",
       "Name: Count, Length: 282, dtype: int64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "counts_by_year_and_sex = (baby\n",
    " .groupby(['Year', 'Sex']) # Arg to groupby is a list of column names\n",
    " ['Count']\n",
    " .sum()\n",
    ")\n",
    "counts_by_year_and_sex "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notice how the code closely follows the grouping recipe.\n",
    "\n",
    "The `counts_by_year_and_sex` series has what we call a multilevel index with\n",
    "two levels, one for each column that was grouped. It's a bit easier to see if\n",
    "we convert the series to a dataframe:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>Count</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Year</th>\n",
       "      <th>Sex</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">1880</th>\n",
       "      <th>F</th>\n",
       "      <td>83929</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>M</th>\n",
       "      <td>110490</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1881</th>\n",
       "      <th>F</th>\n",
       "      <td>85034</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019</th>\n",
       "      <th>M</th>\n",
       "      <td>1785527</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">2020</th>\n",
       "      <th>F</th>\n",
       "      <td>1581301</td>\n",
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       "    <tr>\n",
       "      <th>M</th>\n",
       "      <td>1706423</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>282 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            Count\n",
       "Year Sex         \n",
       "1880 F      83929\n",
       "     M     110490\n",
       "1881 F      85034\n",
       "...           ...\n",
       "2019 M    1785527\n",
       "2020 F    1581301\n",
       "     M    1706423\n",
       "\n",
       "[282 rows x 1 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# The result only has one column\n",
    "counts_by_year_and_sex.to_frame()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are two levels to the index because we grouped by two columns. It can be\n",
    "a bit tricky to work with multilevel indices, so we can reset the index to go\n",
    "back to a dataframe with a single index:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
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       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Year</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Count</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1880</td>\n",
       "      <td>F</td>\n",
       "      <td>83929</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1880</td>\n",
       "      <td>M</td>\n",
       "      <td>110490</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1881</td>\n",
       "      <td>F</td>\n",
       "      <td>85034</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>279</th>\n",
       "      <td>2019</td>\n",
       "      <td>M</td>\n",
       "      <td>1785527</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>280</th>\n",
       "      <td>2020</td>\n",
       "      <td>F</td>\n",
       "      <td>1581301</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>281</th>\n",
       "      <td>2020</td>\n",
       "      <td>M</td>\n",
       "      <td>1706423</td>\n",
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       "     Year Sex    Count\n",
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       "..    ...  ..      ...\n",
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       "281  2020   M  1706423\n",
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       "[282 rows x 3 columns]"
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     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
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    "counts_by_year_and_sex.reset_index()"
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  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Custom Aggregation Functions\n",
    "\n",
    "After grouping, `pandas` gives us flexible ways to aggregate the data. So far,\n",
    "we've seen how to use `.sum()` after grouping:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Year\n",
       "1880     194419\n",
       "1881     185772\n",
       "1882     213385\n",
       "         ...   \n",
       "2018    3487193\n",
       "2019    3437438\n",
       "2020    3287724\n",
       "Name: Count, Length: 141, dtype: int64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "(baby\n",
    " .groupby('Year')\n",
    " ['Count']\n",
    " .sum() # aggregate by summing\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`pandas` also supplies other aggregation functions, like `.mean()`, `.size()`,\n",
    "and `.first()`. Here's the same grouping using `.max()`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Year\n",
       "1880     9655\n",
       "1881     8769\n",
       "1882     9557\n",
       "        ...  \n",
       "2018    19924\n",
       "2019    20555\n",
       "2020    19659\n",
       "Name: Count, Length: 141, dtype: int64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "(baby\n",
    " .groupby('Year')\n",
    " ['Count']\n",
    " .max() # aggregate by taking the max within each group\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "But sometimes `pandas` doesn't have the exact aggregation function we want to\n",
    "use. In these cases, we can define and use a custom aggregation function.\n",
    "`pandas` lets us do this through `.agg(fn)`, where `fn` is a function that we\n",
    "define.\n",
    "\n",
    "For instance, if we want to find the difference between the largest and\n",
    "smallest values within each group (the range of the data), we could first\n",
    "define a function called `data_range`, then pass that function into `.agg()`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Year\n",
       "1880     9650\n",
       "1881     8764\n",
       "1882     9552\n",
       "        ...  \n",
       "2018    19919\n",
       "2019    20550\n",
       "2020    19654\n",
       "Name: Count, Length: 141, dtype: int64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "# The input to this function is a pd.Series object containing a single column\n",
    "# of data. It gets called once for each group.\n",
    "def data_range(counts):\n",
    "    return counts.max() - counts.min()\n",
    "\n",
    "(baby\n",
    " .groupby('Year')\n",
    " ['Count']\n",
    " .agg(data_range) # aggregate using custom function\n",
    ")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We start by defining a `count_unique` function that counts the number of\n",
    "unique values in a series. Then we pass that function into `.agg()`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Year\n",
       "1880     1889\n",
       "1881     1829\n",
       "1882     2012\n",
       "        ...  \n",
       "2018    29619\n",
       "2019    29417\n",
       "2020    28613\n",
       "Name: Name, Length: 141, dtype: int64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "# Since this function is short, we could use a lambda instead\n",
    "def count_unique(s):\n",
    "    return len(s.unique())\n",
    "\n",
    "unique_names_by_year = (baby\n",
    " .groupby('Year')\n",
    " ['Name']\n",
    " .agg(count_unique) # aggregate using the custom count_unique function\n",
    ")\n",
    "unique_names_by_year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
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stroke-opacity: 1; stroke-width: 1px;\"/><g class=\"ytick\"><text text-anchor=\"end\" x=\"57.6\" y=\"4.199999999999999\" transform=\"translate(0,139.3)\" style=\"font-family: 'Open Sans', verdana, arial, sans-serif; font-size: 12px; fill: rgb(36, 36, 36); fill-opacity: 1; white-space: pre; opacity: 1;\">10k</text></g><g class=\"ytick\"><text text-anchor=\"end\" x=\"57.6\" y=\"4.199999999999999\" style=\"font-family: 'Open Sans', verdana, arial, sans-serif; font-size: 12px; fill: rgb(36, 36, 36); fill-opacity: 1; white-space: pre; opacity: 1;\" transform=\"translate(0,85.93)\">20k</text></g><g class=\"ytick\"><text text-anchor=\"end\" x=\"57.6\" y=\"4.199999999999999\" style=\"font-family: 'Open Sans', verdana, arial, sans-serif; font-size: 12px; fill: rgb(36, 36, 36); fill-opacity: 1; white-space: pre; opacity: 1;\" transform=\"translate(0,32.56)\">30k</text></g></g><g class=\"overaxes-above\"/></g></g><g class=\"polarlayer\"/><g class=\"smithlayer\"/><g class=\"ternarylayer\"/><g class=\"geolayer\"/><g class=\"funnelarealayer\"/><g class=\"pielayer\"/><g class=\"iciclelayer\"/><g class=\"treemaplayer\"/><g class=\"sunburstlayer\"/><g class=\"glimages\"/><defs id=\"topdefs-fa1ecf\"><g class=\"clips\"/></defs><g class=\"layer-above\"><g class=\"imagelayer\"/><g class=\"shapelayer\"/></g><g class=\"infolayer\"><g class=\"g-gtitle\"/><g class=\"g-xtitle\"><text class=\"xtitle\" x=\"203\" y=\"239.20625\" text-anchor=\"middle\" style=\"font-family: 'Open Sans', verdana, arial, sans-serif; font-size: 14px; fill: rgb(36, 36, 36); opacity: 1; font-weight: normal; white-space: pre;\">Year</text></g><g class=\"g-ytitle\" transform=\"translate(3.0654296875,0)\"><text class=\"ytitle\" transform=\"rotate(-90,10.934375000000003,101)\" x=\"10.934375000000003\" y=\"101\" text-anchor=\"middle\" style=\"font-family: 'Open Sans', verdana, arial, sans-serif; font-size: 14px; fill: rgb(36, 36, 36); opacity: 1; font-weight: normal; white-space: pre;\"># unique names</text></g></g></svg>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "px.line(unique_names_by_year.reset_index(),\n",
    "        x='Year', y='Name',\n",
    "        labels={'Name': '# unique names'},\n",
    "        width=350, height=250)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We see that the number of unique names has generally increased over time, even\n",
    "though the number of babies born annually has plateaued since the 1960s."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Pivoting\n",
    "\n",
    "Pivoting is essentially a convenient way to arrange the results of a group and\n",
    "aggregation when grouping with two columns. Earlier in this section we grouped\n",
    "the baby names data by year and sex:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
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       "    <tr>\n",
       "      <th>Year</th>\n",
       "      <th>Sex</th>\n",
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       "      <th>M</th>\n",
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       "      <th rowspan=\"2\" valign=\"top\">2020</th>\n",
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       "      <th>M</th>\n",
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       "<p>282 rows × 1 columns</p>\n",
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       "            Count\n",
       "Year Sex         \n",
       "1880 F      83929\n",
       "     M     110490\n",
       "1881 F      85034\n",
       "...           ...\n",
       "2019 M    1785527\n",
       "2020 F    1581301\n",
       "     M    1706423\n",
       "\n",
       "[282 rows x 1 columns]"
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    "counts_by_year_and_sex = (baby\n",
    " .groupby(['Year', 'Sex']) \n",
    " ['Count']\n",
    " .sum()\n",
    ")\n",
    "counts_by_year_and_sex.to_frame()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This produces a `pd.Series` with the counts. We can also imagine the same data\n",
    "with the `Sex` index level \"pivoted\" to the columns of a dataframe. It's easier\n",
    "to see with an example:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
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       "      <td>100738</td>\n",
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       "      <th>1882</th>\n",
       "      <td>99699</td>\n",
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       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>2018</th>\n",
       "      <td>1676884</td>\n",
       "      <td>1810309</td>\n",
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       "    <tr>\n",
       "      <th>2019</th>\n",
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       "Sex         F        M\n",
       "Year                  \n",
       "1880    83929   110490\n",
       "1881    85034   100738\n",
       "1882    99699   113686\n",
       "...       ...      ...\n",
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       "2019  1651911  1785527\n",
       "2020  1581301  1706423\n",
       "\n",
       "[141 rows x 2 columns]"
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     "execution_count": 24,
     "metadata": {},
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   ],
   "source": [
    "mf_pivot = pd.pivot_table(\n",
    "    baby,\n",
    "    index='Year',   # Column to turn into new index\n",
    "    columns='Sex',  # Column to turn into new columns\n",
    "    values='Count', # Column to aggregate for values\n",
    "    aggfunc=sum)    # Aggregation function\n",
    "mf_pivot\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notice that the data values are identical in the pivot table and the table\n",
    "produced with `.groupby()`; the values are just arranged differently. Pivot\n",
    "tables are useful for quickly summarizing data using two attributes and are\n",
    "often seen in articles and papers.\n",
    "\n",
    "The `px.line()` function also happens to work well with pivot tables,\n",
    "since the function draws one line for each column of data in the table:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
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            "gridcolor": "rgb(232,232,232)",
            "linecolor": "rgb(36,36,36)",
            "showgrid": false,
            "showline": true,
            "ticks": "outside"
           },
           "bgcolor": "white",
           "caxis": {
            "gridcolor": "rgb(232,232,232)",
            "linecolor": "rgb(36,36,36)",
            "showgrid": false,
            "showline": true,
            "ticks": "outside"
           }
          },
          "title": {
           "x": 0.5,
           "xanchor": "center"
          },
          "width": 350,
          "xaxis": {
           "automargin": true,
           "gridcolor": "rgb(232,232,232)",
           "linecolor": "rgb(36,36,36)",
           "showgrid": true,
           "showline": true,
           "ticks": "outside",
           "title": {
            "standoff": 15
           },
           "zeroline": false,
           "zerolinecolor": "rgb(36,36,36)"
          },
          "yaxis": {
           "automargin": true,
           "gridcolor": "rgb(232,232,232)",
           "linecolor": "rgb(36,36,36)",
           "showgrid": true,
           "showline": true,
           "ticks": "outside",
           "title": {
            "standoff": 15
           },
           "zeroline": false,
           "zerolinecolor": "rgb(36,36,36)"
          }
         }
        },
        "width": 350,
        "xaxis": {
         "anchor": "y",
         "autorange": true,
         "domain": [
          0,
          1
         ],
         "range": [
          1880,
          2020
         ],
         "title": {
          "text": "Year"
         },
         "type": "linear"
        },
        "yaxis": {
         "anchor": "x",
         "autorange": true,
         "domain": [
          0,
          1
         ],
         "range": [
          -31208.444444444423,
          2271540.4444444445
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         "title": {
          "text": "Value"
         },
         "type": "linear"
        }
       }
      },
      "image/png": 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    }
   ],
   "source": [
    "fig = px.line(mf_pivot, width=350, height=250)\n",
    "fig.update_traces(selector=1, line_dash='dashdot')\n",
    "fig.update_yaxes(title='Value')"
   ]
  },
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   "metadata": {},
   "source": [
    "This section covered common ways to aggregate data in `pandas` using\n",
    "the `.groupby()` function with one or more columns, or using\n",
    "the `pd.pivot_table()` function.\n",
    "In the next section, we'll explain how to join dataframes together."
   ]
  }
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